Adiponectin Is a Candidate Biomarker of Lower Extremity Bone Density in Men With Chronic Spinal Cord Injury
Bibliographic record
Abstract
Adipose tissue is a major regulator of bone metabolism and in the general population obesity is associated with greater bone mineral density (BMD). However, bone-fat interactions are multifactorial, and may involve pathways that influence both bone formation and resorption with competing effects on the skeleton. One such pathway involves adipocyte production of adipokines that regulate bone metabolism. In this study we determined the association between BMD, walking status, and circulating adipokines (adiponectin and leptin) in 149 men with chronic spinal cord injury (SCI). Although adipokine levels did not vary significantly based on walking status, there was a significant inverse association between adiponectin and BMD in wheelchair users independent of body composition. We found no association between adiponectin and BMD in the walkers and no association between leptin and BMD in either group. These findings suggest that for subjects with chronic SCI, walking may mitigate the effect of adiponectin mediated bone loss. For wheelchair users, adipose-derived adiponectin may contribute to SCI-induced osteoporosis because the osteoprotective benefits of obesity appear to require mechanical loading during ambulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".